An error correcting decoder is typically implemented, e.g., in a network system, to reduce communication errors. One type of an error correcting decoder is an iterative error correcting decoder. Iterative error correcting decoders typically use a large-scale parallel network of nodes performing soft probability calculation. These nodes exchange probability information of a received data block among one another. After a certain number of iterations within an iterative decoder structure, individual noisy information in a data block (or word) is transformed into an estimate of the word as a whole. Examples of iterative decoders are the low density parity check (LDPC) decoders, Hamming decoders, Reed-Solomon decoders, Turbo decoders, and the like.
The structure of an iterative error correcting decoder can be represented graphically by a factor graph. A factor graph consists of nodes and edges, where the edges represent wire connections between the nodes, and a node represents a function of its inputs. For example, in a low density parity check (LDPC) factor graph, there are two types of nodes representing two distinct functions—i.e., “equality constraint” nodes and “parity check” nodes. According to the IEEE 802.3ae (10 GBASE-T) standard, the proposed LDPC decoder consists of (2048) equality constraint nodes and (384) parity check nodes. Each equality constraint node has (6) bidirectional connections to corresponding parity constraint nodes and each parity check node has a total of (32) bidirectional connections to corresponding equality constraint nodes. This results in a factor graph with network matrix of (12,228) connections. The probabilities associated with received bit values iterate between these two node functions to finally resolve the most probable value of each data bit.
LDPC code is specified by a parity check matrix (which is commonly referred to as an H matrix) having a very few number of “ones” per row. An example H matrix 100 is shown in FIG. 1. The length of each codeword is equal to the number of columns in the H matrix 100. In one example, each codeword is created such that the parity of each set of bits corresponding to the “ones” in a row is even. The number of rows corresponds to the number of parity checks that the codeword must satisfy. Therefore, if all errors in a received codeword are corrected by the decoder, all parity checks must be satisfied for the output codeword.
An important feature of one implementation of an iterative decoder is the number of iterations that the iterative decoder can perform on an input codeword in a given amount of time as it relates to the bit error rate (BER) of the iterative decoder. A higher number of iterations results in a better BER performance of an iterative decoder. Therefore, to maximize the performance of a single iterative decoder, it is generally preferred to have a given iterative decoder perform a higher number of iterations to go through a certain number of equality constraint and parity check nodes (which determines the BER performance of the iterative decoder). Accordingly, there is a trade off between the number of iterations an iterative decoder can perform in a time interval of each data codeword versus the power and complexity of the iterative decoder. In a digital iterative decoder, one can increase the clock frequency, increase the gate sizes, add more flip-flops between logic stages, adopt different implementation architectures, and/or run at higher supply voltage in order to get more iterations per codeword at cost of more power. More iterations can also be achieved by pipelining two or more iterative decoders in series, so that one iterative decoder works on the decoded output codeword of the prior iterative decoder. Such an approach, however, translates into more area and power.